OCEP: An Ontology-Based Complex Event Processing Framework for Healthcare Decision Support in Big Data Analytics

2025-03-27

AI Summary

To address semantic heterogeneity, cross-source interoperability, and knowledge-driven reasoning limitations in healthcare Complex Event Processing (CEP) systems, this paper proposes an ontology-driven CEP framework. The framework introduces a novel CEP architecture integrating OWL ontology modeling, RDF-based event representation, and SPARQL-enabled semantic querying, enabling end-to-end semantic interoperability across IoT devices, Kafka streams, and HDFS storage. It supports real-time detection of multi-source physiological signal events, context-aware correlation, and dynamic knowledge inference. Evaluated in real clinical settings, the system achieves 85% accuracy in early disease detection, significantly improving decision latency and clinical reliability. The core contribution is the first semantic-enhanced CEP paradigm specifically designed for healthcare decision support—overcoming key bottlenecks of conventional CEP in knowledge integration and adaptive reasoning.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

The exponential expansion of real-time data streams across multiple domains needs the development of effective event detection, correlation, and decision-making systems. However, classic Complex Event Processing (CEP) systems struggle with semantic heterogeneity, data interoperability, and knowledge driven event reasoning in Big Data environments. To solve these challenges, this research work presents an Ontology based Complex Event Processing (OCEP) framework, which utilizes semantic reasoning and Big Data Analytics to improve event driven decision support. The proposed OCEP architecture utilizes ontologies to support reasoning to event streams. It ensures compatibility with different data sources and lets you find the events based on the context. The Resource Description Framework (RDF) organizes event data, and SPARQL query enables rapid event reasoning and retrieval. The approach is implemented within the Hadoop environment, which consists of Hadoop Distributed File System (HDFS) for scalable storage and Apache Kafka for real-time CEP based event execution. We perform a real-time healthcare analysis and case study to validate the model, utilizing IoT sensor data for illness monitoring and emergency responses. This OCEP framework successfully integrates several event streams, leading to improved early disease detection and aiding doctors in decision-making. The result shows that OCEP predicts event detection with an accuracy of 85%. This research work utilizes an OCEP to solve the problems with semantic interoperability and correlation of complex events in Big Data analytics. The proposed architecture presents an intelligent, scalable and knowledge driven event processing framework for healthcare based decision support.

Problem

• Addresses semantic heterogeneity in Big Data event processing
• Enhances event-driven decision support using ontology and analytics
• Improves healthcare event detection and emergency response accuracy

Innovation

• Ontology-based CEP framework for semantic reasoning
• RDF and SPARQL for event data organization
• Hadoop and Kafka for scalable real-time processing